Alexa Spilios Theodoratos, CTO of BISEES Information Systems Ltd Speaks to CIO Bulletin: ‘The Competitive Advantage is our Architecture that Puts an End to the Trade-Offs and Brings it all Together
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Spilios Theodoratos, CTO of BISEES Information Systems Ltd Speaks to CIO Bulletin: ‘The Competitive Advantage is our Architecture that Puts an End to the Trade-Offs and Brings it all Together

Spilios Theodoratos, CTO of BISEES Information Systems Ltd Speaks to CIO Bulletin: ‘The Competitive Advantage is our Architecture that Puts an End to the Trade-Offs and Brings it all Together

Big Data is the enormous amount of structured, semi-structured, and unstructured data that are exponentially generated by high-performance applications in almost all companies. Large-scale data gathering and analytics are quickly becoming the new frontier of competitive differentiation. Organizations want to use large-scale data gathering and analytics to shape strategy. The question is, do they get the maximum value of these gatherings? Has the return on investment (ROI) of the spending on big data materialized? Do they grow in new areas at an accelerated pace because of big data? Or they feel they have a monolithic infrastructure that is just a cost line?

BISEES Information Systems Ltd. offers an innovative approach to overcome this threat. The increasingly competitive landscape and cyclical nature of the business requires timely access to accurate business information. Technical and organizational challenges associated with ‘big data’ and advanced analytics make it difficult to build in-house applications; this ends up as ineffective solutions and becomes paralyzed into inaction. In short, the BISEES solution is used to develop applications that could perform deep analysis on huge amounts of data utilizing Artificial Neural Networks (ANNs).

BISEES was founded in 2014 and is based in Dublin, Ireland.

Interview Excerpt: Spilios Theodoratos

What was the motive behind setting up the company?

With long experience in big corporations and financial institutions, the founders understand the necessity to facilitate the exploration of corporate data. Financial institutions, at that time, invested in building large data warehouses, and various data repositories spread across the organizations. That became the norm for all businesses. Business analysts were struggling to get context and insights out of disparate data sources and raw data, while Business Intelligence (BI) solutions could not provide them with the intelligence they wanted. While there was, clearly, a shift from traditional BI reports to exploratory systems, with more user-friendly visualizations, the agony to understand the data and get the right answers promptly, remained.

The idea was to help organizations to analyse and understand anything, anywhere, instantly, accurately, affordably. This can only happen if we combine the Intelligence, of the business analysts, with well-organised data, augmented with Artificial Intelligence, in seamless integration. It is what we call Collaborative Intelligence (CI). Organizations need to understand and materialise AI hype, exploring pragmatic, down-to-earth, smart applications that bring value. A few years ago, when I attended a course at MIT Sloan Business School, I remember the MIT Center for Collective Intelligence director, Professor Thomas W. Malone, who was saying that both hype about AI’s immediate potential and fear about its effects are catastrophic. Our purpose is to simplify AI and bring monetary value and growth to our clients. We help them apply Artificial Intelligence Techniques (Neural Networks, etc.) to their Big Data infrastructures and get unprecedented insights into their corporate data. We transform their Data warehouses into Creative Warehouses.

Tell us about your awarded product?

In the era of data monetization, we offer an end-to-end analytical solution for hassle-free enablement. Our solution is based on an awarded Data Model, ready to answer most of the business needs. Whether our clients cater with analyzing the performance, finding opportunities to expand or increase the profit, our solution has answers to all.

We help businesses to set data up for success. It’s time to let data drive the business. Data has power, and it’s time to unleash this power. We bring data together so that teams can come and work together to shape a new smart organization. Innovation doesn’t happen in isolation, and if until now, the hard work was to create a data community within the organization. The next step is to combine the intelligence of this community with AI. United, we will achieve the true digital transformation. We simplify and achieve one place for all the data and one system to combine BI and AI. The competitive advantage is our architecture that puts an end to the trade-offs and brings it all together.

Teams in a silo cannot innovate fast enough on their own. Our world is changing more rapidly than ever before, and data and time is value. With Google, we partnered and managed to create a unique cloud-based solution that is simple but very powerful to use. Put all your data, all your analytics, and AI in one platform and combine the insight through a unified visualization layer. No more silos of Data Warehouse and Data Lake. When you can combine both, only then businesses uncover what we call the “hidden insight”

We call our product Exepno Performance Management system. Exepno is the Greek word for Intelligence. Our system enables the massive data transformation to bring all our users and all our data together and give them the means to draw insights from the data. We bring a single enterprise data platform built on GCP that scales across every department and every team. Actually we are Global Build Partners with Google and that helps us deliver anywhere in the world.

Why should Companies work with BISEES Information Systems on this new data journey?

BISEES is a member of the MIT Enterprise Forum of Cambridge, MA, and a Red Herring top 100 Europe Winner. We have developed an innovative solution that was first implemented in the Financial Institutions sector and now is expanding to many other industries. Our flagship product is based on a business information model that provides an associative approach. It satisfies the need for fast, accurate, and responsive decision-making.

We take the big data infrastructure to a new level. We have developed innovative Neural Networks that can be used for forecasting and other use cases. As you know, one of the major application areas of ANNs is forecasting. There is an increasing interest in forecasting using ANNs in recent years. Forecasting has a long history, and the importance of this old subject is reflected by the diversity of its applications in different disciplines ranging from business to engineering. The ability to accurately predict the future is fundamental to many decision processes in planning, scheduling, purchasing, strategy formulation, policymaking, and supply chain operations. As such, forecasting is an area where a lot of efforts have been invested in the past. Yet, it is still an important and active field of human activity at present and will continue to be in the future.

We combine Big Data and Neural networks in a new way. Every organization has vast amounts of data. But no one knows their importance. Our first step is specifically to estimate their importance or weights.

ANNs can “learn from data or experience,” which is highly desirable in various forecasting situations where data are usually easy to collect. Still, the underlying data-generating mechanism is not known or pre-specifiable. Neural networks have been mathematically shown that they can accurately approximate many types of complex functional relationships. This is an important and powerful characteristic, as any forecasting model aims to accurately capture the functional relationship between the variable to be predicted and other relevant factors or variables.

Where do you focus most on a new implementation?

We focus on security, and the protection of data is among our primary design criteria. Security drives our organizational structure, training priorities, and hiring processes. It shapes the data centers we use and the technology they house. Google has selected us to become a partner because we share the same dedication to secure our customers’ data. We had to get certified by Google and get their credentials based on the correct handling of data and our processes. At the beginning of a new project, we explain to new customers that data protection is more than just security. Google’s strong contractual commitments ensure you maintain control over your data and how it is processed, including the assurance that your data is not used for advertising or any purpose other than to deliver Google Cloud services.

How is ANNs-approach useful for pattern recognition problems?

Neural networks have solved a wide range of business forecasting problems. Some of these application areas such as forecasting accounting earnings, predicting bankruptcy and business failure, forecasting consumer choice, market share, marketing category, and marketing trends, forecasting consumer demand, traffic, inventory, new product development project success, IT project escalation, product demand or sales, and retail sales or even energy consumption of buildings, and air quality.

ANNs, in some cases, gives better insights than human intelligence. In ANN models, there are no assumptions about data properties or data distribution. Therefore, ANNs are more useful in practical application. Also, unlike some statistical models that require certain hypothesis for testing, ANN models do not require any hypothesis. ANNs are very flexible data reduction models, encompassing nonlinear regression models and discriminant models. Unlike the support vector machine, extreme learning machine, and random forest, ANNs are more fault-tolerant. That is, they can handle incomplete data and noise and solve non-linear problems. Also, trained ANNs can generalize at high speed and make predictions. Furthermore, ANNs are scalable compared to the support vector machine, extreme learning machine, and random forest. Unlike the support vector machine, extreme learning machine, and random forest, ANNs are more fault-tolerant. That is, they can handle incomplete data and noise and solve non-linear problems. Also, trained ANNs can generalize at high speed and make predictions. Furthermore, ANNs are scalable compared to the support vector machine, extreme learning machine, and random forest.

A Relentlessly Reliable Leader

Spilios Theodoratos serves as the Chief Technology Officer of BISEES Information Systems Ltd.

“Our flagship product is based on a business information model that provides an associative approach. It satisfies the need for fast, accurate, and responsive decision-making.”

info@bisees.com


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